Big Data Study for Gluten-Free Foods in India and USA Using Online Reviews and Social Media
Bibliographic record
Abstract
Celiac disease, gluten-allergy or gluten-sensitivity is caused due to glutamine protein from the grains like wheat, rye and barley (collectively called gluten). This protein damages the small intestine and causes stomach pain, bloating, weakness etc. Celiac disease, gluten-allergy or gluten-sensitivity has never really been taken seriously in developing countries like India. However, in developed nations like UK, USA, Canada and other parts of Europe, gluten-free foods have become quite popular. With a prevalence rate of about one in 100 - 133 people worldwide, celiac disease is widespread across the globe and life-long consumption of gluten-free food is recommended treatment for this allergy. Apart from celiac-disease patients, gluten-free foods are also consumed by health conscious people for weight management and high protein diet and by the patients for diabetes, autism and food allergies. Apart from gluten-free flour, biscuits, cookies and snacks, product innovations like gluten-free beers are becoming very popular. Big data including online blogs, articles, and reviews have played a major role in increased sales of gluten-free foods. Thus, analysis of editorial and social media content becomes essential to understand the leading trends in gluten-free foods. This study provided deep insights about positive, negative and neutral sentiments related to gluten-free foods using the data from Perspectory Media Insights and Google Trends. This study also revealed that most of the consumers talked and expected product innovation in food sections like snacks, fast food (pizza, pasta and noodles) and desserts through comments on social and editorial media. Searches were divided into developed (e.g., U.S.A.) and developing nations (e.g., India) to get more details about the consumer preferences. This study would help manufacturers of gluten-free foods to develop food products according to the choices and preferences of consumers. The study is very unique in itself since it combines big data to niche food market of gluten-free foods to draw the valuable consumer preferences using online platforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".